Using available data from a consumption survey and contamination data on ochratoxin A (OA) in food, a sensitivity analysis (SA) for high quantiles (95th and 99th quantiles) of OA exposure distribution was carried out, obtained by a Monte Carlo simulation in French children. Exposure assessment for food contaminants is important to control the risk of foodborne diseases. Risk assessors are interested in high quantiles of contaminant exposure distributions. As these exposure distributions are generally very asymmetrical, it is difficult to obtain relevant and stable high quantiles in such a context. Determining OA exposure distribution is complex because it is based on the sum of elementary exposure distributions (eight foodstuffs are analysed here), and each one of these is the product of a consumption distribution and a contamination distribution. The SA enables us to quantify the influences of the parameter variability of the consumption and contamination probability density functions (pdf) which have been fitted to the data, our simulation model inputs, on the 95th and 99th quantiles of the output exposure distribution. After some preliminary trials, we have postulated a quadratic polynomial regression model for the quantiles of OA exposure distribution in view of undertaking this SA. This regression model comprises 32 main factors, their 496 two-factor interactions and their 32 quadratic terms. The 32 factors are the parameters of the fitted pdf: 16 parameters of Gamma distributions relative to the eight consumed foods and 16 parameters of Gamma distributions relative to the eight food OA contaminations. For an optimal parameter estimation of such a large model, we used an experimental design approach depending on a resolution-V fractional factorial design of 6561 experiments. The factor ranges are established by a preliminary study of bootstrap sampling. From the bootstrap samples, the factor ranges are obtained taking into account the correlation between the two parameters of the fitted Gamma pdf. A full exposure distribution is simulated for each of the 6561 experiments. The consumption dependencies are taken into account by the Iman and Conover method. On the basis of this analysis, validated and useful models for each desired quantile are obtained showing a major influence of the parameters of "Cereals" (consumption and contamination) and slightly less so for parameter of "Pork" consumption in the sensitivity of the quantiles.